DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications on 10/06/2023
Claims 1-23 pending in the application
Claims 1-23 rejected
Priority
No claims to domestic or foreign priority made on Application Data Sheet received on 10/26/2022.
Information Disclosure Statement
Information Disclosure Statement received on 10/06/2023 accepted and considered by examiner.
Drawings
Responsive to drawings received on 01/31/2023
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
(302) in figure 3 is missing.
(402) in figure 4 is missing.
(502) in figure 5 is missing.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
(408B) not mentioned in specifications.
(408C) not mentioned in specifications.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Responsive to abstract received on 01/31/2023. Abstract contains 150 words and no legal or implied phraseology. Abstract is accepted by the examiner.
Responsive to specification received on 01/31/2023. Specification is accepted by the examiner.
Claim Objections
Responsive to claims received on 10/26/2022.
Claims 2 and 14 are objected to because of the following informality: claim 2 states “further wherein affecting the execution of the at least one application includes providing at information in the form of an alert or a message.” The word “at” should be deleted.
Claims 12 and 18 are objected to due to the following typo: “, wherein the swarm-level topology section” likely was meant to be written as “, wherein the swarm-level topology selection” to match the word usage in claim 11.
Appropriate correction is required.
Claim Interpretation
Swarm-level topology selection: From par 44 of the specifications “Furthermore, guides can be used to adjust numerous variations of a particle swarm algorithm. For example, a weight of a signal from one or more particles, as well as the radius, as well as a selection of a respective topology (i.e., a connectivity scheme) can be adjusted. An example guide can be a value representing multi-ring or group topologies, in which information value can be shared. Selection of a respective topology can depend at least in part on its connectivity, such as a wheel topology may be more appropriate for high value information, while a ring topology may be more appropriate for low value.“ This selection is choosing between different connectivity schemes such as between a ring or wheel topology.
Claim 17: wherein determining the information value further comprises: The examiner is interpreting that the steps of claim 17 are not used to determine the information value, but are occurring separately as a result of the determination. This is based on par 103: “In one or more implementations, optimization meta-algorithms can operate to fine-tune the particle swarm algorithm during run-time, based on the information value, such as relating to exchanged positions and non-positional data. Examples can include a weighting value of a signal received from at least some of the plurality of particles, at least one radius of connectivity, a group/subgroup or swarm-level topology selection, and a number of subgroups, groups, neighborhoods, clans or rings with which a respective one of the plurality of particles shares information is usable for such fine tuning.” The specifications do not suggest that these extra limitations determine the information value, instead, the specification suggests that the information value may be used to tune these parameters.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 2, 3, 14, and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 2 and 14 pertain to using machine learning to generate the additional information referenced in claims 1 and 13. Par 76 states: “A machine learning model can be developed (e.g., trained) to retrieve historical particle swarm algorithm data for training (e.g., through supervised learning). As a result, the machine learning can be used to optimize performance (e.g., through predicting accurate sizing requirements and reconfiguring resource allocations accordingly). It is to be appreciated that several of the logical operations described herein are implemented as a sequence of computer- implemented acts or program modules running on one or more computing devices. “ … par 102: “In one or more implementations of the present disclosure, machine learning can be used to calculate information value using historical data and application specific data.”
The specifications do not disclose an algorithm or list of steps explaining how machine learning or neural networks can be used to generate additional information (i.e.: local exploration space characteristics, number of previous iterations, or a percentage of space explored from the plurality of particles). Instead the specification seems to imply that the machine learning could be used to generate “information value” as well as to optimize or modify the particle swarm algorithm. The MPEP 2161.01 (I) states “original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed.” Therefore, these claims lack written description as the specification does not provide support for machine learning being used to generate “additional information” in the form of “local exploration space characteristics, number of previous iterations, or a percentage of space explored from the plurality of particles” as outlined in the claims.
Claims 3 and 15 are rejected as being dependent on claims 2 and 14 respectively.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 7, 19, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 19 recites the limitation “, the information value for a respective mode of operation for sharing the information value.” There is insufficient antecedent basis for this limitation in the claim.
Claims 7 and 20 are rejected for being dependent on claims 6 and 19 respectively.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claim 1:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites “A method for optimizing a particle swarm process during execution of at least one application running on at least one computing device, comprising:” which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 1 recites: receiving
The examiner interprets the broadest reasonable interpretation of receiving particle information to be an observation of particles in a swarm process to determine information relating to that particle. For example, a particle in a 2D space position value can be defined as a Euclidian distance from a global minimum. Among two particles, one of those particles will have the “best group position.” Under broadest reasonable interpretation, this claim limitation encompasses performing a Euclidian distance calculation of two particles to a global minimum in a 2D vector space, and then observing which position is best. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Further, The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Since this claim under broadest reasonable interpretation is an observation which can be performed with pen and paper, this claim recites an abstract idea.
receiving,
Under broadest reasonable interpretation, this claim encompasses receiving a number of previous iterations for a particle swarm process. This is an observation of how many passed iterations have occurred in a process, which is simply counting how many iterations have passed. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to an observation of how many iterations have passed, this claim recites an abstract idea.
determining,
This claim limitation is understood as determining an information value (a value) using the particle information (a positional value) and additional information (a value of number iterations or percentage of space explored). Under broadest reasonable interpretation, this claim can be interpreted as either a mathematic calculation, or a mental judgement. For instance, the past 10 iterations have seen no increase in fitness, so the information value will be large to promote higher convergence. Or, only 10% space has been explored, so the information value will promote a higher spread. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a judgement, the claim recites an abstract idea.
sharing,
As the examiner understands, sharing in this context constitutes a modification of a particle’s parameters, i.e.: velocity, based on the information value received. Position in this context refers to the particles position equation which is modified by the velocity. Where Movement in this context is the modification of the position equation based on the velocity equation. These steps as outlined are calculations of math equations. MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Since this claim limitation can be interpreted as determining the movement equation for a particle through a mathematic calculation, this claim limitation is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. throughout the claim limitations, the claim recites “by the at least one computing device”
This computing device performs the abstract ideas listed above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, this limitation does not integrate the exception into a practical application.
Claim 1 also recites and affecting, by the at least one computing device, execution of the at least one application as a function of movement of each of the at least one of the plurality of particles.
This limitation states that the abstract ideas above are to be used to execute an application. This claim does not outline how this application is to be executed based on the movement of particles, only that this application is executed as a response. The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’”. Furthermore, this claim limitation does not limit the applicability of the claim to any field of endeavor, nor does it limit how the algorithm can be applied to any of those fields. The MPEP 2106.05(f)(3) states “A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more. For instance, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect, amounts to a claim that is merely adding the words "apply it" to the judicial exception.“ Therefore, this claim does not integrate the exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, the additional elements in this claim are mere instructions to apply the exception. Therefore, these limitations in the claim do not provide significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 1 is not eligible under 35 USC 101.
Claim 2
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 1 which is a process
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 2 recites: wherein the additional information is generated by machine learning using at least one of historical data and application-specific data, and
This limitation recites using machine learning to generate the additional information, which is “of local exploration space characteristics, a number of previous iterations, and a percentage or amount of space explored by at least some of the plurality of particles” The claim does not provide any detailed about how the machine learning model operates or how the generation is done, only that is uses historical and application-specific data. The recitation of “historical and application-specific” is a general claim limitation, since “historical and application-specific” applies to all fields. The broadest reasonable interpretation of using machine learning to generate additional information, like an amount of space explored, covers the performance of a mathematical calculation. MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. Because this claim limitation pertains to a mathematic limitation, the claim recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 2 additionally recites further wherein affecting the execution of the at least one application includes providing at information in the form of an alert or a message.
The examiner believes affecting the execution of an application by “providing at information in the form of an alert or a message” to be an insignificant application of the judicial exception. One example of an Insignificant application to a judicial exception given in MPEP 2106.05(g) is “Printing or downloading generated menus.” This limitation is similar to the example above, as it can be understood as simply displaying the generated information. Therefore this limitation does not integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception?
NO. As stated in Step 2A Prong 2, The claim limitation is a form of insignificant application as discussed in the MPEP. Therefore this limitation does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 2 is not eligible under 35 USC 101.
Claim 3
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 2 which is a process
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 3 recites Wherein the machine learning is implemented by at least one neural network-based architecture.
The claim does not provide any details about how the neural network based architecture operates or how the neural network architecture is used to generate the additional information. The plain meaning of generating additional information encompasses a mental process or evaluation (i.e.: a user doing an optimization iteration by iteration keeping track of how many iterations pass.) MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore this claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 3 does not recite any additional elements that could integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. Claim 3 does not recite additional elements that amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 3 is not eligible under 35 USC 101.
Claim 4
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 1 which is a process
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
using,
Using information value to adjust topological and operational characteristics during an iteration of the particle swarm algorithm under broadest reasonable interpretation encompasses modifying how particles in the swarm are connected as well as how they share information. With respect to claim 1, this would be the equivalent to observing an information value, and then changing how the information is shared between two particles as a result. For instance, an information value which suggests that a large portion of the space has been explored may lead to a judgement to change topology to increase neighborhood size as well as increase the social factor weights for velocity in the particle as an operational characteristic. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation under broadest reasonable interpretation is a judgement for how to change the particles characteristics, this limitation pertains to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 4 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of claim 4, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 4 is not eligible under 35 USC 101.
Claim 5
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 1, wherein determining the information value further comprises: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 5 recites calculating,
The calculation of positional data under broadest reasonable interpretation includes a calculation of a Euclidian distance between two particles. The calculation of non-positional data under broadest reasonable interpretation includes a calculation of the number of connections a particle contains. MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. Because this claim limitation pertains to a mathematic calculation it is considered to fall under an abstract idea.
altering,
As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user would want the exchange of information to have a greater weight to improve consolidation rather than spread. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 5 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 5 is not eligible under 35 USC 101.
Claim 6
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 1, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 6 recites using,
As the examiner understands under broadest reasonable interpretation, using the information value for a respective mode of operation for sharing the information value, encompasses sharing the information competitively or cooperatively. This means that the information is able to be shared among all the particles (cooperatively) or only shared among the members of a subswarm (competitively). As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may decide to use a cooperative mode of operation to exchange information to have a greater weight to improve consolidation rather than spread. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 6 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 6 is not eligible under 35 USC 101.
Claim 7
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 6, which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 7 recites wherein the respective mode of operation includes a collaborative mode of operation and a competitive mode of operation.
As the examiner understands under broadest reasonable interpretation, using the information value for a respective mode of operation for sharing the information value, encompasses sharing the information competitively or cooperatively. This means that the information is able to be shared among all the particles (cooperatively) or only shared among the members of a subswarm (competitively). As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may decide to use a cooperative mode of operation to exchange information to have a greater weight to improve consolidation rather than spread. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. The claim does not recite additional elements beyond what already discussed.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. The claim does not recite additional elements beyond what already discussed.
Based on the above facts, the office concludes that claim 7 is not eligible under 35 USC 101.
Claim 8:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 1, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 8 recites using,
Under broadest reasonable interpretation, the examiner understands using the information value to force groups to “disperse, randomize and/or assign at least one of the plurality of particles to a different subgroup. “ is to change how the information is shared among groups. For example, to assign a particle to a different subgroup is to state that the particles information is no longer shared with its previous group and is now shared with a new group. As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may move particles to consolidate them into larger groups. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 8 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 8 is not eligible under 35 USC 101.
Claim 9
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 1, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 9 recites adjusting,
Under broadest reasonable interpretation, the examiner understands using the information value to force groups to “disperse, randomize and/or assign at least one of the plurality of particles to a different subgroup. “ is to change how the information is shared among groups. For example, to assign a particle to a different subgroup is to state that the particles information is no longer shared with its previous group and is now shared with a new group. As stated earlier under claim 1, the exchange of information/sharing of information in this context is interpreted as deciding which particles should receive/update their respective equations (i.e.: position, velocity, etc.). For example, if a large percentage of the search space has been explored, a user may move particles to consolidate them into larger groups. As described this is a judgement. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a mental process, the claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 8 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 9 is not eligible under 35 USC 101.
Claim 10:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 9, further comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 10 recites ranking,
Under the broadest reasonable interpretation, the examiner understands ranking subgroups based on effectiveness to be a judgement performed by a user. For example, a user may judge the subgroup with the highest fitness scores to have a higher ranking. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation relates to a judgement (the mental process of ranking things) this claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 10 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea of the claim, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The use of a generic computer to apply the judicial exception does not provide significantly more
Based on the above facts, the office concludes that claim 10 is not eligible under 35 USC 101.
Claim 11
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A method for optimizing a particle swarm algorithm at run-time using information value, the method comprising: Which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 11 recites: receiving
The examiner interprets the broadest reasonable interpretation of receiving particle information to be an observation of particles in a swarm process to determine information relating to that particle. For example, a particle in a 2D space position value can be defined as a Euclidian distance from a global minimum. Among two particles, one of those particles will have the “best group position.” Under broadest reasonable interpretation, this claim limitation encompasses performing a Euclidian distance calculation of two particles to a global minimum in a 2D vector space, and then observing which position is best. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Further, The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Since this claim under broadest reasonable interpretation is an observation which can be performed with pen and paper, this claim recites an abstract idea.
receiving,
Under broadest reasonable interpretation, this claim encompasses receiving a number of previous iterations for a particle swarm process. This is an observation of how many passed iterations have occurred in a process, which is simply counting how many iterations have passed. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because this limitation pertains to an observation of how many iterations have passed, this claim recites an abstract idea.
determining,
This claim limitation is understood as determining an information value (a value) using the particle information (a positional value) and additional information (a value of number iterations or percentage of space explored). Under broadest reasonable interpretation, this claim can be interpreted as either a mathematic calculation, or a mental judgement. For instance, the past 10 iterations have seen no increase in fitness, so the information value will be large to promote higher convergence. Or, only 10% space has been explored, so the information value will promote a higher spread. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a judgement, the claim recites an abstract idea.
determining, by the at least one computing device, characteristic information representing at least one of:
-a weight of a signal received from at least some of the plurality of particles;
-at least one radius of connectivity;
-a group of particles, a subgroup of particles, or a swarm-level topology selection; and a number of subgroups, groups, neighborhoods, clans or rings with which respective ones of the plurality of particles share information;
This claim limitation is understood as determining some information from the particle swarm algorithm which is referred to as characteristic information. This claim can be interpreted as an observation, a mathematic calculation, or a mental judgement. For instance, determining a weight of a signal received is simply observing the mathematic equations used in the particles that correlate to signal weight. Determining a radius of connectivity is an observation of what mathematic equation is used in determining neighborhood sizes (i.e.: Euclidian distance) or observing the largest distance between any two particles in a swarm. Determining a group of particles in which information is shared can be done based on a judgement, i.e.: the particles will be grouped based on Euclidian distance or ring topology, where the information will be shared among the neighbors of each particle. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a judgement, the claim recites an abstract idea. Since under broadest reasonable interpretation this claim falls under mental processes of observations and judgement, this claim pertains to an abstract idea.
altering, by the at least one computing device, the particle swarm algorithm as a function of the characteristic information,
Under broadest reasonable interpretation, this examiner understands altering the algorithm in this context to be modifying the mathematic equations which represent the particles in the swarm. For instance, increasing information propagation is an increase in the weight parameter for a particle correlating to social movement in its velocity. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Where the examiner understands “altering” as a textual replacement for performing a mathematical operation.
wherein altering the particle swarm algorithm includes at least one of:
- optimizing, by the at least one computing device using the information value, specific information that is exchanged between particles;
-increasing or decreasing information propagation in a hierarchy of particles;
-changing the information value based on storage of significant positions;
-optimizing a number of historical stored positions based on the information value;
-changing at least one particle group assignment;
-and optimizing randomization using the information value.
As stated above, altering the particle swarm algorithm under broadest reasonable interpretation is a textual replacement for performing a mathematical operation. Optimizing information exchanged between particles is modifying the equations that govern particle information exchange. Increasing/decreasing information propagation in a hierarchy is also modifying the equations that govern particle information exchange. Changing an information value based on storage of significant positions is an observation of significant positions and then changing the value, which is a mathematic calculation (i.e.: increase the value by 1). Optimizing a number of historical stored positions is a judgement made to govern the behavior of the algorithm, i.e.: I would like the particles to move towards the last 3 historical best positions saved. Changing one particle group assignment is a judgement to determine which group a particle should be in depending on various factors, and optimizing randomization is also a is a judgement to determine how a particle should move depending on various factors (I.e.: particles have begun to converge early, increase randomization).
The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” The MPEP 2106.04(a)(2)(III) also states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Because these claim limitations pertain to recitations of math, as well as mental observations and judgements, this claim is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 11 additionally recites by the at least one computing device
As stated under claim 1, The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Since this limitation is the use of a generic computing device to apply the abstract idea, it does not integrate the judicial exception into a practical idea.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, Since this limitation is the use of a generic computing device to apply the abstract idea, it does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 11 is not eligible under 35 USC 101.
Claim 12
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The method of claim 11 which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 12 inherits the limitations of claim 11, and therefore is directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 12 additionally recites wherein the swarm-level topology section includes at least one of two connections per node and all connected nodes.
As the examiner understands, this claim limitation pertains to the swarm-level topology selection, where the selection includes having two connections per node or all nodes connected. See claim interpretation section. This limitation is insignificant activity. The MPEP 2106.05(g)(2) considers “Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).” When considering if a limitation is insignificant activity. A swarm level topology including a selection of connections per node or all connected nodes is nominally and tangentially related to the invention, as all particle swarm optimization algorithms contain swarm topologies with connections inherently. The MPEP 2106.05(g)(3) also considers “Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).” As stated previously, all particle swarm optimization algorithms contain swarm topologies with connections inherently, so this step of selecting connections per node is a necessary data gathering step. Because this limitation in the claim is insignificant activity, it does not integrate the exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The limitation above is insignificant activity. Under step 2B The MPEP 2106.05(g)(1) considers Whether the extra-solution limitation is well known. In “Topology Selection for Particle Swarm Optimization” by Liu et al. 2016, the researches state “The global best (gbest) topology and the local best (lbest) topology are two common social topologies in PSO. In the gbest topology, each particle is connected with all other particles (Examiner note: all connected nodes), i.e., it is a fully connected graph. In the lbest topology, each particle is only connected with its nearest K neighbors. In most publications and in this paper, K=2 is used (Examiner note: two connections per node) without loss of generality.” Since this limitation is common, it does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 12 is not eligible under 35 USC 101.
Claim 13:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A computer implemented system which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
Claim 13 contains effectively the same claim limitations as claim 1 and is therefore directed to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 13 contains effectively the same claim limitations as claim 1 and is therefore does not integrate the judicial exception into a practical application.
Additionally claim 13 states A computer implemented system for optimizing a particle swarm optimization process during execution of at least one application running on at least one computing device, the system comprising:
at least one computing device configured by executing instructions stored on non-transitory processor readable media to perform steps including:
The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Because this limitation in the claim states that the abstract exceptions are performed on computer components, this limitation does not integrate a judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, The inclusion of generic computer components to implement an abstract idea does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 13 is not eligible under 35 USC 101.
Claim 14:
Claim 14 is an effective duplicate of claim 2 with the only difference being that it depends on claim 13. Due to the reasons discussed on claim 2 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 14 is not eligible under 35 USC 101.
Claim 15:
Claim 15 is an effective duplicate of claim 3 with the only difference being that it depends on claim 13. Due to the reasons discussed on claim 3 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 15 is not eligible under 35 USC 101.
Claim 16:
Claim 16 is an effective duplicate of claim 4 with the only difference being that it depends on claim 13. Due to the reasons discussed on claim 4 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 16 is not eligible under 35 USC 101.
Claim 17
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites The system of claim 13, wherein determining the information value further comprises: which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 17 recites: calculating, by the at least one computing device:
a weight of a signal received from at least some of the plurality of particles;
at least one radius of connectivity;
a topology selection;
and a number of subgroups, groups, neighborhoods, clans or rings with which a respective one of the plurality of particles shares information.
This claim limitation is understood as calculating some information from the particle swarm algorithm during the determination step. This claim can be interpreted as a mathematic calculation. For instance, calculating a weight of a signal received is simply adding up the mathematic equations used in the particles that correlate to signal weight. calculating a radius of connectivity is an observation of what mathematic equation is used in determining neighborhood sizes (i.e.: Euclidian distance) and performing the calculation or measuring the largest distance between any two particles in a swarm. Calculating a topology selection as well as a number of subgroups to share information is a judgment on topology and information sharing based on a calculation. i.e. a large space has been explored, the topology will be modified to have more connections between particles. MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Since this limitation pertains to a calculation and judgement, the claim recites an abstract idea. Since under broadest reasonable interpretation this claim falls under mental processes of observations and judgement as well as mathematic calculation, this claim pertains to an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 17 does not recite additional elements that could integrate the judicial exception into a practical application.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. Claim 17 does not recite additional elements that could amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 17 is not eligible under 35 USC 101.
Claim 18:
Claim 18 is an effective duplicate of claim 12 with the only difference being that it depends on claim 17. Due to the reasons discussed on claim 12 and claim 17, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 18 is not eligible under 35 USC 101.
Claim 19:
Claim 19 is an effective duplicate of claim 6 with the only difference being that it depends on claim 13. Due to the reasons discussed on claim 6 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 19 is not eligible under 35 USC 101.
Claim 20:
Claim 20 is an effective duplicate of claim 7 with the only difference being that it depends on claim 13. Due to the reasons discussed on claim 7 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 20 is not eligible under 35 USC 101.
Claim 21:
Claim 21 is an effective duplicate of claim 8 with the only difference being that it depends on claim 13. Due to the reasons discussed on claim 8 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 21 is not eligible under 35 USC 101.
Claim 22:
Claim 22 is an effective duplicate of claim 9 with the only difference being that it depends on claim 13. Due to the reasons discussed in claim 9 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 22 is not eligible under 35 USC 101.
Claim 23:
Claim 23 is an effective duplicate of claim 10 with the only difference being that it depends on claim 22. Due to the reasons discussed in claim 1 and claim 13, this claim is directed to an abstract idea, does not integrate the judicial exception into a practical application, and does not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 23 is not eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-13, and 16-23 are rejected under 35 U.S.C. 103 as being unpatentable over Computational Intelligence: An Introduction, Second Edition A.P. Engelbrech. Chapter 16 (Engelbrecht_2007)
Claim 1:
EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization makes obvious Receiving, by at least one computing device from each of a plurality of particles exploring a design space (page 289 par 2: “In PSO, individuals, referred to as particles, are “flown” through hyperdimensional search space.”) during a particle swarm algorithm iteration, particle information representing at least one of a best particle position, a best group position, and a local best position;
(page 294 par 2: “The social component, c2r2(yˆ −xi), in the case of the gbest PSO or, c2r2(yˆi − xi), in the case of the lbest PSO, which quantifies the performance of particle i relative to a group of particles, or neighbors. Conceptually, the social component resembles a group norm or standard that individuals seek to attain. The effect of the social component is that each particle is also drawn towards the best position found by the particle’s neighborhood.”) Examiner note: Where it is inherent that particle information must be received when a particle is drawn towards a best position found by the particle’s neighborhood
receiving, by the at least one computing device during the particle swarm algorithm iteration from the plurality of particles, additional information representing at least one of local exploration space characteristics, a number of previous iterations, and a percentage or amount of space explored by at least some of the plurality of particles; (page 298 par 6: “The following stopping conditions have been used: • Terminate when a maximum number of iterations, or FEs, has been exceeded.” (Examiner note: where it is inherent that in order for the algorithm to stop when a maximum number of iterations has been reached, that the number of previous iterations is being received by the computing device).
sharing, by the at least one computing device, the information value to at least one of the plurality of particles; determining, by the at least one computing device for each of the at least one of the plurality of particles, a respective position to move, wherein the respective position is determined at least using the shared information value, and further wherein each of the at least one of the plurality of particles moves based on the determined respective position to move;
par 290: “similar to a population, while a particle is similar to an individual. In simple terms, the particles are “flown” through a multidimensional search space, where the position of each particle is adjusted according to its own experience and that of its neighbors. Let xi(t) denote the position of particle i in the search space at time step t; unless otherwise stated, t denotes discrete time steps. The position of the particle is changed by adding a velocity, vi(t), to the current position, i.e. xi(t + 1) = xi(t) + vi(t + 1)”
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Examiner note: where velocity is the information value.
EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization does not expressly recite A method for optimizing a particle swarm process during execution of at least one application running on at least one computing device, comprising:
determining, by the at least one computing device using the particle information and the additional information, an information value;
and affecting, by the at least one computing device, execution of the at least one application as a function of movement of each of the at least one of the plurality of particles.
EngelBrecht_2007 in section 16.3 Basic Variations, however, makes obvious determining, by the at least one computing device using the particle information and the additional information, an information value;
page 306 par 4: “The inertia weight, w, controls the momentum of the particle by weighing the contribution of the previous velocity – basically controlling how much memory of the previous flight direction will influence the new velocity. For the gbest PSO, the velocity equation changes from equation (16.2) to vij(t + 1) = wvij(t) + c1r1j(t)[yij(t) − xij(t)] + c2r2j(t)[ˆyj(t) − xij(t)” … page 307 par 5: “Linear decreasing, where an initially large inertia weight (usually 0.9) is linearly decreased to a small value (usually 0.4). From Naka et al. [619], Ratnaweera et al. [706], Suganthan [820], Yoshida et al. [941] w(t) = (w(0) − w(nt)) (nt − t) nt + w(nt) (16.26) where nt is the maximum number of time steps for which the algorithm is executed, w(0) is the initial inertia weight, w(nt) is the final inertia weight, and w(t) is the inertia at time step t. Note that w(0) > w(nt).” ) Examiner note: Where the information value is particle velocity. This velocity is determined by the particle information (moves towards gbest) and is determined by the additional information (the time step) which influences the inertia weight component of the velocity.)
EngelBrecht_2007 in section 16.3 Basic Variations and EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 in section 16.3 Basic Variations and EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization
The rationale for doing so would have been to follow a motivation proposed in the art. EngelBrecht_2007 in section 16.3 Basic Variations states tin par 306 par 5: “The inertia weight was introduced by Shi and Eberhart [780] as a mechanism to control the exploration and exploitation abilities of the swarm, and as a mechanism to eliminate the need for velocity clamping [227]. The inertia weight was successful in addressing the first objective, but could not completely eliminate the need for velocity clamping. The inertia weight, w, controls the momentum of the particle by weighing the contribution of the previous velocity – basically controlling how much memory of the previous flight direction will influence the new velocity. “ In order to take advantage of the benefit to control exploration and exploitation abilities of a swarm, the user of EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization would be inclined to add additional information in the form of number of iterations, to control the velocity using inertia. Therefore, it would have been obvious to combine the velocity equation of EngelBrecht_2007 in section 16.3 Basic Variations with the PSO algorithm of EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization for the benefit of controlling exploration and exploitation to obtain the invention as specified in the claims.
EngelBrecht_2007 in section 16.3 Basic Variations and EngelBrecht_2007 in section 16.1 Basic Particle Swarm Optimization do not expressly recite.
A method for optimizing a particle swarm process during execution of at least one application running on at least one computing device, comprising:
and affecting, by the at least one computing device, execution of the at least one application as a function of movement of each of the at least one of the plurality of particles.
EngelBrecht_2007 in section 16.7 Applications, however, makes obvious A method for optimizing a particle swarm process during execution of at least one application running on at least one computing device, comprising: (Page 357 par 2: “The basic algorithm as given in Algorithm 16.17 has been applied successfully to the zero-sum games of tick-tack-toe [283, 580], checkers [284], and bao [156]. Franken and Engelbrecht also applied the approach to the non-zero-sum game, the iterated prisoner’s dilemma [285]. A variant of the approach, using two competing swarms has recently been used to train agents for a probabilistic version of tick-tac-toe [654].” )
and affecting, by the at least one computing device, execution of the at least one application as a function of movement of each of the at least one of the plurality of particles. (Page 357 par 2: “The basic algorithm as given in Algorithm 16.17 has been applied successfully to the zero-sum games of tick-tack-toe [283, 580], checkers [284], and bao [156]. Franken and Engelbrecht also applied the approach to the non-zero-sum game, the iterated prisoner’s dilemma [285]. A variant of the approach, using two competing swarms has recently been used to train agents for a probabilistic version of tick-tac-toe [654].” )
EngelBrecht_2007 sections 16.1, 16.3, and 16.7 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 sections 16.1, 16.3, and 16.7. The rationale for doing so would have been Obvious to try. As stated in EngelBrecht_2007 section 16.7. “PSO has been used mostly to optimize functions with continuous-valued parameters.” And also in reference to algorithm 16.17 it states “Weights are adjusted using the position and velocity updates of any PSO algorithm.”
Therefore, it would have been obvious to combine the application of EngelBrecht_2007 section 16.7 with the velocity weights of EngelBrecht_2007 section 16.3 and basic algorithm of EngelBrecht_2007 16.1 for the benefit of balancing exploration and exploitation to play tick-tack-toe more successfully to obtain the invention as specified in the claims.
Claim 4:
The method of claim 1, further comprising:
EngelBrecht_2007 sections 16.1, 16.3, and 16.7 do not expressly recite using, by the at least one computing device, the information value to adjust topological and operational characteristics during the iteration of the particle swarm algorithm.
EngelBrecht_2007 section 16.5 Single-Solution Particle Swarm Optimization, however, makes obvious using, by the at least one computing device, the information value (Page 327 par 5: “Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms.” … Page 326 par 5: “The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment.” to adjust topological and operational characteristics during the iteration of the particle swarm algorithm. ( page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. (Examiner note: an adjustment of operational characteristics) It can also happen that individuals may migrate between groups.” (Examiner note: an adjustment of topological characteristics)
Examiner note: where the combination of group interaction with the environment and selection of global best is the information value used in this implementation of PSO
EngelBrecht_2007 sections 16.1, 16.3, 16.5, and 16.7 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 section 16.5 and 16.1. The rationale for doing so would have been to follow a teaching proposed in EngelBrecht_2007 section 16.5 page 315: “A variety of PSO variations have been developed, mainly to improve the accuracy of solutions, diversity and convergence behavior. This section reviews some of these variations for locating a single solution to unconstrained, single-objective, static optimization
problems.” Therefore, it would have been obvious to combine EngelBrecht_2007 section 16.5 and 16.1 for the benefit of locating single solutions in optimizations problems more efficiently to obtain the invention as specified in the claims.
Claim 5:The method of claim 1, wherein determining the information value further comprises:
EngelBrecht_2007 section 16.5 makes obvious calculating, by at least one computing device for each of the particles, positional data and non-positional data; and further comprising:
page 317 par 7: “Braendler and Hendtlass [81] proposed a variation on the spatial neighborhoods implemented by Suganthan, where particles move towards neighboring particles that have found a good solution. Assuming a minimization problem, the neighborhood of particle i is defined as the nN particles with the smallest value of E(xi, xi ) × f(xi ) (16.48) where E(xi, xi ) is the Euclidean distance between particles (Examiner note: positional data) i and i , and f(xi ) is the fitness of particle i (Examiner note: non-position data). Note that this neighborhood calculation mechanism also allows for overlapping neighborhoods.”
altering, by the at least one computing device as a function of the determined information value (page 318 par 4 : “The search is initialized with an lbest PSO with nN = 2 (i.e. with the smallest neighborhoods). The neighborhood sizes are then increased with increase in iteration until each neighborhood contains the entire swarm (i.e. nN = ns)., (Examiner note: where the presence of lbest and iterations is the information value) exchange of information between at least two of the plurality of particles. (page 318 par 4 : “The search is initialized with an lbest PSO with nN = 2 (i.e. with the smallest neighborhoods). The neighborhood sizes are then increased with increase in iteration until each neighborhood contains the entire swarm (i.e. nN = ns). (Examiner note: Where a change in neighborhood size is a change in exchange of information between particles)
Claim 6:
The method of claim 1, further comprisingEngelBrecht_2007 section 16.5 however makes obvious: using, by the at least one computing device, the information value (page 327 par 1: “For their multi-phase PSO (MPPSO), the velocity update is defined as [16, 17]: vij(t + 1) = wvij(t) + c1xij(t) + c2ˆyj(t) (16.76) The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle’s position is only updated if the new position results in improved performance. Let the tuple (w, c1, c2) represent the values of the inertia weight, w, and acceleration oefficients c1 and c2. Particles that find themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1, c2) = (1,−1, 1). Particles in phase 2 have (w, c1, c2) = (1, 1,−1), forcing them to move away from the global best position. Sub-swarms switch phases either• when the number of iterations in the current phase exceeds a user specified threshold, or”) for a respective mode of operation for sharing the information value. Page 326 under 16.5.4 Sub-Swarm based PSO par 1: “A number of cooperative and competitive PSO implementations that make use of multiple swarms have been developed. Some of these are described below”
Claim 7:
The method of claim 6,
EngelBrecht_2007 section 16.5 makes obvious wherein the respective mode of operation includes a collaborative mode of operation and a competitive mode of operation.
Page 326 under 16.5.4 Sub-Swarm based PSO par 1: “A number of cooperative and competitive PSO implementations that make use of multiple swarms have been developed. Some of these are described below”
Claim 8:
The method of claim 1, further comprising: EngelBrecht_2007 section 16.5 makes obvious using, by the at least one computing device, the information value (Page 327 par 5: “Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms.” … Page 326 par 5: “The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment.”) to force some subgroups to disperse, randomize and/or assign at least one of the plurality of particles to a different subgroup. (page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. It can also happen that individuals may migrate between groups.”)
Claim 9:
The method of claim 1, further comprising:EngelBrecht_2007 section 16.5 makes obvious adjusting, by the at least one computing device as a function of the information value, value (Page 327 par 5: “Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms.” … Page 326 par 5: “The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment.”) at least one subgroup of the plurality of particles to disperse, randomize, or be assigned to at least one different subgroup. (page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. It can also happen that individuals may migrate between groups.”)
Claim 10:The method of claim 9, further comprising:
EngelBrecht_2007 section 16.5 makes obvious ranking, by the at least one computing device, the at least one subgroup of the plurality of particles based on the at least one subgroup's effectiveness.
Page 321 algorithm 16.5: “Calculate the fitness of all particles; for each particle i = 1,...,ns do Randomly select nts particles; Score the performance of particle i against the nts randomly selected particles; end Sort the swarm based on performance scores; Replace the worst half of the swarm with the top half, without changing the personal best positions”
Claim 11:
EngelBrecht_2007 section 16.1 makes obvious A method for optimizing a particle swarm algorithm at run-time using information value, the method comprising: (page 290: “In simple terms, the particles are “flown” through a multidimensional search space, where the position of each particle is adjusted according to its own experience and that of its neighbors. Let xi(t) denote the position of particle i in the search space at time step t; unless otherwise stated, t denotes discrete time steps. The position of the particle is changed by adding a velocity, vi(t), to the current position, i.e. xi(t + 1) = xi(t) + vi(t + 1) (16.1) with xi(0) ∼ U(xmin, xmax). It is the velocity vector that drives the optimization process, and reflects both the experiential knowledge of the particle and socially exchanged information from the particles neighborhood.”
Receiving, by at least one computing device from each of a plurality of particles exploring a design space (page 289 par 2: “In PSO, individuals, referred to as particles, are “flown” through hyperdimensional search space.”)
during a particle swarm algorithm iteration, particle information representing at least one of a best particle position, a best group position, and a local best position; (page 294 par 2: “The social component, c2r2(yˆ −xi), in the case of the gbest PSO or, c2r2(yˆi − xi), in the case of the lbest PSO, which quantifies the performance of particle i relative to a group of particles, or neighbors. Conceptually, the social component resembles a group norm or standard that individuals seek to attain. The effect of the social component is that each particle is also drawn towards the best position found by the particle’s neighborhood.”) Examiner note: Where it is inherent that particle information must be received when a particle is drawn towards a best position found by the particle’s neighborhood
receiving, by the at least one computing device during the particle swarm algorithm iteration from the plurality of particles, additional information representing at least one of local exploration space characteristics, a number of previous iterations, and a percentage or amount of space explored by at least some of the plurality of particles; (page 298 par 6: “The following stopping conditions have been used: • Terminate when a maximum number of iterations, or FEs, has been exceeded.” (Examiner note: where it is inherent that in order for the algorithm to stop when a maximum number of iterations has been reached, that the number of previous iterations is being received by the computing device).
EngelBrecht_2007 section 16.1 does not expressly recite
determining, by the at least one computing device using the particle information and the additional information an information value;
determining, by the at least one computing device, characteristic information representing at least one of:
-a weight of a signal received from at least some of the plurality of particles;
-at least one radius of connectivity;
-a group of particles, a subgroup of particles, or a swarm-level topology selection;
-and a number of subgroups, groups, neighborhoods, clans or rings with which respective ones of the plurality of particles share information;
altering, by the at least one computing device, the particle swarm algorithm as a function of the characteristic information,
wherein altering the particle swarm algorithm includes at least one of:
-optimizing, by the at least one computing device using the information value, specific information that is exchanged between particles;
-increasing or decreasing information propagation in a hierarchy of particles;
-changing the information value based on storage of significant positions;
-optimizing a number of historical stored positions based on the information value;
-changing at least one particle group assignment;
-and optimizing randomization using the information value.
EngelBrecht_2007 section 16.5 however makes obvious determining, by the at least one computing device using the particle information and the additional information an information value; (page 327 par 1: “For their multi-phase PSO (MPPSO), the velocity update is defined as [16, 17]: vij(t + 1) = wvij(t) + c1xij(t) + c2ˆyj(t) (16.76) The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle’s position is only updated if the new position results in improved performance. Let the tuple (w, c1, c2) represent the values of the inertia weight, w, and acceleration coefficients c1 and c2. Particles that find themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1, c2) = (1,−1, 1). Particles in phase 2 have (w, c1, c2) = (1, 1,−1), forcing them to move away from the global best position. Sub-swarms switch phases either• when the number of iterations in the current phase exceeds a user specified threshold,) Examiner note: Where the information value (velocity) is determined based on the global best (particle information) and number of iterations (information value).
determining, by the at least one computing device, characteristic information representing at least one of:
-a weight of a signal received from at least some of the plurality of particles;
-at least one radius of connectivity;
-a group of particles, a subgroup of particles, or a swarm-level topology selection;
-and a number of subgroups, groups, neighborhoods, clans or rings with which respective ones of the plurality of particles share information;
(page 326 par 5: “Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a different task, or exhibits a different behavior. The behavior of a group or task performed by a group usually changes over time in response to the group’s interaction with the environment. It can also happen that individuals may migrate between groups.”) Examiner note: Where this is characteristic information representing a subgroup of particles).
altering, by the at least one computing device, the particle swarm algorithm as a function of the characteristic information,
wherein altering the particle swarm algorithm includes at least one of:
-optimizing, by the at least one computing device using the information value, specific information that is exchanged between particles;
-increasing or decreasing information propagation in a hierarchy of particles;
-changing the information value based on storage of significant positions;
-optimizing a number of historical stored positions based on the information value;
-changing at least one particle group assignment;
-and optimizing randomization using the information value.
(par 327: “Sub-swarms switch phases either• when the number of iterations in the current phase exceeds a user specified threshold, or • when particles in any phase show no improvement in fitness during a user specified number of consecutive iterations. Examiner note: a change in particle group assignment)
EngelBrecht_2007 section 16.1 and 16.5 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 sections 16.1 and 16.5 The rationale for doing so would have been to follow a teaching and motivation proposed in the art. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 section 16.5 and 16.1. The rationale for doing so would have been to follow a teaching proposed in EngelBrecht_2007 section 16.5 page 315: “A variety of PSO variations have been developed, mainly to improve the accuracy of solutions, diversity and convergence behavior. This section reviews some of these variations for locating a single solution to unconstrained, single-objective, static optimization problems.” Therefore, it would have been obvious to combine EngelBrecht_2007 section 16.5 and 16.1 for the benefit of locating single solutions in optimizations problems more efficiently to obtain the invention as specified in the claims.
Claim 12:
The method of claim 11,
EngelBrecht_2007 sections 16.1 and 16.5 do not expressly recite wherein the swarm-level topology section includes at least one of two connections per node
EngelBrecht_2007 section 16.2 Social Network Structures, however, makes obvious wherein the swarm-level topology section includes at least one of two connections per node(Page 301: “Different social network structures have been developed for PSO and empirically studied.” … “The ring social structure, where each particle communicates with its nN immediate neighbors. In the case of nN = 2, a particle communicates with its immediately adjacent neighbors .“) and all connected nodes. (page 301: “The star social structure, where all particles are interconnected as illustrated in Figure 16.4(a). Each particle can therefore communicate with every other particle. In this case each particle is attracted towards the best solution found by the entire swarm.)
EngelBrecht_2007 sections 16.1, 16.2 and 16.5 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 sections 16.1, 16.2 and 16.5
The rationale for doing so would have been to follow a teaching in the art. EngelBrecht_2007 section 16.2 in page 301 states “Each particle therefore imitates the overall best solution. The first implementation of the PSO used a star network structure, with the resulting algorithm generally being referred to as the gbest PSO. The gbest PSO has been shown to converge faster than other network structures, but with a susceptibility to be trapped in local minima. The gbest PSO performs best for unimodal problems.” … “Since information flows at a slower rate through the social network, convergence is slower, but larger parts of the search space are covered compared to the star structure. This behavior allows the ring structure to provide better performance in terms of the quality of solutions found for multi-modal problems than the star structure. The resulting PSO algorithm is generally referred to as the lbest PSO.”
Therefore, it would have been obvious to combine the algorithms and neighborhood dynamics of EngelBrecht_2007 section 16.1 and 16.5 with the presence of different topology selections of EngelBrecht_2007 section 16.2 for the benefit of solving either unimodal or multimodal problems more efficiently to obtain the invention as specified in the claims.
Claim 13:
The limitations of claim 13 are substantially the same as those of claim 1 and are therefore rejected due to the same reasons as outlined above for claim 1.
Additionally, EngelBrecht_2007 section 16.7 makes obvious the additional limitations of . A computer implemented system for optimizing a particle swarm optimization process during execution of at least one application running on at least one computing device, the system comprising: (page 356 par 5: “Messerschmidt and Engelbrecht [580] developed a PSO approach to train NNs in a coevolutionary mechanism to approximate the evaluation function of leaf nodes in a game tree as described in Section 15.2.3. The initial model was applied to the simple game of tick-tack-toe”)
at least one computing device configured by executing instructions stored on non-transitory processor readable media to perform steps including:
Title: “Computational Intelligence” … page 357 par 1: “algorithm, summarized in Algorithm 16.17, a swarm of particles is randomly created, where each particle represents a single NN. Each NN plays in a tournament against a group of randomly selected opponents, selected from a competition pool (usually consisting of all the current particles of the swarm and all personal best positions). After each NN has played against a group of opponents, it is assigned a score based on the number of wins, losses and draws achieved. These scores are then used to determine personal best and neighborhood best solutions. Weights are adjusted using the position and velocity updates of any PSO algorithm.”) Examiner note: Where this implies an makes obvious to one ordinarily skilled in the art that this algorithm is being performed on a computing device where computing devices perform functions stored on non-transitory processors.
Claim 16:
The limitations of claim 16 are substantially the same as those of claim 4 except that it depends from claim 13 and are therefore rejected due to the same reasons as outlined above for claim 4 and claim 13.
Claim 17:
The system of claim 13, wherein determining the information value further comprises:
calculating, by the at least one computing device:
EngelBrecht_2007 section 16.1 makes obvious and a number of subgroups, groups, neighborhoods, clans or rings with which a respective one of the plurality of particles shares information.
Page 292 par 4: “Selection of neighborhoods is done based on particle indices. However, strategies have been developed where neighborhoods are formed based on spatial similarity (refer to Section 16.2). There are mainly two reasons why neighborhoods based on particle indices are preferred: 1. It is computationally inexpensive, since spatial ordering of particles is not required. For approaches where the distance between particles is used to form neighborhoods, it is necessary to calculate the Euclidean distance between all pairs of particles, which is of O(n2 s) complexity. 2. It helps to promote the spread of information regarding good solutions to all particles, irrespective of their current location in the search space. It should also be noted that neighborhoods overlap. A particle takes part as a member of a number of neighborhoods. This interconnection of neighborhoods also facilitates the sharing of information among neighborhoods, and ensures that the swarm converges on a single point, namely the global best particle. The gbest PSO is a special case of the lbest PSO with nNi = ns.”
EngelBrecht_2007 section 16.2 makes obvious a topology selection;
par 301: “For sparsely connected networks with a large amount of clustering in neighborhoods, it can also happen that the search space is not covered sufficiently to obtain the best possible solutions. Each cluster contains individuals in a tight neighborhood covering only a part of the search space. Within these network structures there usually exist a few clusters, with a low connectivity between clusters. Consequently information on only a limited part of the search space is shared with a slow flow of information between clusters. Different social network structures have been developed for PSO and empirically studied.” Examiner note: Where a selection of different social networks is a topology selection.
EngelBrecht_2007 section 16.2 makes obvious at least one radius of connectivity;
par 317 par 5: “Neighborhoods are usually formed on the basis of particle indices. That is, assuming a ring social network, the immediate neighbors of a particle with index i are particles with indices (i − 1 mod ns) and (i − 1 mod ns), where ns is the total number of particles in the swarm. Suganthan proposed that neighborhoods be formed on the basis of the Euclidean distance between particles [820]. For neighborhoods of size nN , the neighborhood of particle i is defined to consist of the nN particles closest to particle i. Algorithm 16.4 summarizes the spatial neighborhood selection process.” Examiner note: Where the Euclidian distance from the last particle forms the last radius of connectivity.
Where it would be obvious for one ordinarily skilled in the art to combine EngelBrecht_2007 sections 16.2, 16.5, and 16.1 for reasons already stated above.
EngelBrecht_2007 sections 16.2, 16.5, 16.7 and 16.1 do not expressly recite a weight of a signal received from at least some of the plurality of particles;
EngelBrecht_2007 section 16.3 however makes obvious a weight of a signal received from at least some of the plurality of particles; page 308: “Clerc proposes an adaptive inertia weight approach where the amount of change in the inertia value is proportional to the relative improvement of the swarm [134]. The inertia weight is adjusted according to wi(t + 1) = w(0) + (w(nt) − w(0)) emi(t) − 1 emi(t) + 1 (16.29) where the relative improvement, mi, is estimated as mi(t) = f(yˆi(t)) − f(xi(t)) f(yˆi(t)) + f(xi(t)) (16.30) with w(nt) ≈ 0.5 and w(0) < 1. Using this approach, which was developed for velocity updates without the cognitive component, each particle has its own inertia weight based on its distance from the local best (or neighborhood best) position. The local best position, yˆi(t) can just as well be replaced with the global best position yˆ(t). Clerc motivates his approach by considering that the more an individual improves upon his/her neighbors, the more he/she follows his/her own way, and vice versa. Clerc reported that this approach results in fewer iterations [134]” Examiner note: Where the inertia Is adjusted in proportion to distance from a local best position (ie weight of a signal from other particles)
EngelBrecht_2007 sections 16.1, 16.2, 16.3, 16.5 and 16.7 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine EngelBrecht_2007 sections 16.1, 16.2, 16.3, 16.5 and 16.7 The rationale for doing so would have been to follow a teaching as proposed in the art. EngelBrecht_2007 section 16.3 states page 307 par 1: “These approaches usually start with large inertia values, which decreases over time to smaller values. In doing so, particles are allowed to explore in the initial search steps, while favoring exploitation as time increases.” Therefore, it would have been obvious to combine the PSO implementations and applications of EngelBrecht_2007 sections 16.1, 16.2,16.5 and 16.7 with an adaptive signal of section 16.3 for the benefit of balancing exploration and exploitation to obtain the invention as specified in the claims.
Claim 18:
EngelBrecht_2007 section 16.2 makes obvious The system of claim 17, wherein the topology section includes at least one of two connections per node (Page 301: “Different social network structures have been developed for PSO and empirically studied.” … “The ring social structure, where each particle communicates with its nN immediate neighbors. In the case of nN = 2, a particle communicates with its immediately adjacent neighbors as illustrated in Figure 16.4(b). Each particle attempts to imitate its best neighbor by moving closer to the best solution found within the neighborhood.) and all nodes connected. (page 301: “The star social structure, where all particles are interconnected as illustrated in Figure 16.4(a). Each particle can therefore communicate with every other particle. In this case each particle is attracted towards the best solution found by the entire swarm.)
Claim 19:
The limitations of claim 19 are substantially the same as those of claim 6 except that it depends from claim 13 and are therefore rejected due to the same reasons as outlined above for claim 6 and claim 13.
Claim 20:
The limitations of claim 20 are substantially the same as those of claim 7 except that it depends from claim 13 and are therefore rejected due to the same reasons as outlined above for claim 7 and claim 13.
Claim 21:
The limitations of claim 21 are substantially the same as those of claim 8 except that it depends from claim 13 and are therefore rejected due to the same reasons as outlined above for claim 8 and claim 13.
Claim 22:
The limitations of claim 22 are substantially the same as those of claim 9 except that it depends from claim 13 and are therefore rejected due to the same reasons as outlined above for claim 9 and claim 13.
Claim 23:
The limitations of claim 23 are substantially the same as those of claim 10 except that it depends from claim 22 and are therefore rejected due to the same reasons as outlined above for claim 10 and claim 13.
Claims 2-3, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Engelbrecht_2007 and further in view of Neural-Guided Particle Swarm Optimization by Benhalem and Lones (Benhalem_2020) and US 20220381433 A1 (Santangeli_2022)
Claim 2:Engelbrecht_2007 recites The method of claim 1, wherein the additional information (See claim 1)
further wherein affecting the execution of the at least one application (see claim 1)
However Engelbrecht_2007 does not explicitly recite:
generated by machine learning using at least one of historical data and application-specific data, and includes providing information in the form of an alert or a message.
Benhalem_2020, however, makes obvious is generated by machine learning (page 3 col 1 par 3: “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm 1. ANN-PSO is outlined in Algorithm 2, with highlighting showing the parts of the algorithm that differ from Algorithm 1. A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer. The number of input neurons is the same as the dimensionality of the problem, and it has one output neuron. At each iteration, each informant’s pbest is used as an input to the ANN, generating a response value from the output neuron (lines 19-23 in Algorithm 2). The pbest with the highest response value is then selected as the gbest used in Equation 1”) using at least one of historical data and application-specific data, and (page 3 col 2 par 1: “Within this evolutionary process, the fitness of an ANN is measured by its cumulative success in guiding particles to better locations, in a manner akin to reinforcement learning.”) Examiner note: Where this process as described is the use of historical data as outlined in the specification. Where a neural-guided PSO algorithm is interpreted as machine learning.
Engelbrecht_2007 and Benhalem_2020 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Engelbrecht_2007 and Benhalem_2020.
The rationale for doing so would have been to follow a motivation proposed in the prior art by Benhalem_2020. Benhalem_2020 page 3 col 2 par 4 states “From these tables, it is evident that PSO-ANN performs better than PSO. The best mean result (shown underlined) is produced by ANN-PSO for every function in this test suite, and this advantage is retained as the dimensionality of the problem increases. This suggests (for these functions at least), that using an ANN to guide PSO is beneficial. For most of the problems, this benefit appears to be quite sizeable.” Benhalem_2020 page 3 col 1 par 4 also states “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm.” As stated, this neural guided PSO builds upon a standard PSO and provides a sizeable benefit to the algorithm. Engelbrecht_2007 discusses using a standard PSO algorithm and ways to optimize such algorithm, such as an inclusion of additional information. Benhalem_2020 does not explicitly teach the inclusion of additional information, but teaches page 3 col 1 par 4: “A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer.” One reasonably skilled in the art would know that a simple perceptron can be applied to other information in the particle swarm application such as additional information. An individual reasonably skilled in the art would have known that using the Neural guided PSO on top of the standard PSO algorithm of Engelbrecht_2007 with additional information would have produced such benefit with a reasonable expectation of success.
Therefore, it would have been obvious to combine the neural guided PSO of Benhalem_2020 with the standard PSO algorithms of Engelbrcht_2007 for the benefit of better performance to obtain the invention as specified in the claims.
Engelbrecht_2007 and Benhalem_2020 do not expressly recite includes providing at information in the form of an alert or a message.
Santangeli_2022, however, makes obvious includes providing at information in the form of an alert or a message. (par 34: “In many implementations, the burner system provides machine learning and optimization of prioritized burner performance, which may be defined by one or more criteria, for example, efficiency, emissions, etc. The system may maintain a history or log of optimized biases and may alert the system operator to trend deviations. The system may notify operators of equipment problems, such as drifting sensor calibrations, off-specification fuel, component wear, malfunction, and/or failure, and the like. The AI may use one or more multivariate analysis tools including learning models and/or particle swarm optimization. Such tools may be used to continually monitor and/or tune performance. The AI may enable progressive improvement and/or reprioritization of performance criteria.”)
Engelbrecht_2007, Benhalem_2020, and Santangeli_2022 are analogous art to the claimed invention because they are from the same field of endeavor called artificial intelligence. Engelbrecht_2007, and Benhalem_2020 are academic sources which describe a particle swarm optimization process and its permutations. Santengeli_2022 applies artificial intelligence (including a particle swarm process) to a burner system. See par 34: “The AI may use one or more multivariate analysis tools including learning models and/or particle swarm optimization.”
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Engelbrecht_2007, Benhalem_2020, and Santangeli_2022.
The rationale for doing so would have been to apply a known technique to a known device ready for improvement to yield a predictable result. This application teaches applying the PSO algorithm as outlined to the execution of some application. Santangeli_2022 teaches the application of a PSO algorithm to a burner system application. Santangeli_2022 also teaches using this system to send alerts to a user. The prior art of Engelbrecht_2007 and Benhalem_2020 contain known particle swarm optimization techniques to improve the application device of Santangeli_2022. One ordinarily skilled in the art would know to apply known techniques of improving PSO algorithms to an application which uses a PSO algorithm. Therefore, it would have been obvious to combine the alert system of Santangeli_2022 with the PSO algorithm improvements of Engelbrecht_2007 and Benhalem_2020 for the benefit of containing a more efficient algorithm system, as well as gaining the benefit of notifying a user of issues in an application to obtain the invention as specified in the claims.
Claim 3:
Engelbrecht_2007 does not expressly recite The method of claim 2, wherein the machine learning is implemented by at least one neural network-based architecture.
Benhalem_2020, however, makes obvious The method of claim 2, wherein the machine learning is implemented by at least one neural network-based architecture. (page 3 col 1 par 3: “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm 1. ANN-PSO is outlined in Algorithm 2, with highlighting showing the parts of the algorithm that differ from Algorithm 1. A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer. The number of input neurons is the same as the dimensionality of the problem, and it has one output neuron. At each iteration, each informant’s pbest is used as an input to the ANN, generating a response value from the output neuron (lines 19-23 in Algorithm 2). The pbest with the highest response value is then selected as the gbest used in Equation 1”)
Engelbrecht_2007 and Benhalem_2020 are analogous art to the claimed invention because they are from the same field of endeavor called particle swarm optimization. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Engelbrecht_2007 and Benhalem_2020.
The rationale for doing so would have been to follow a motivation proposed in the prior art by Benhalem_2020. Benhalem_2020 page 3 col 2 par 4 states “From these tables, it is evident that PSO-ANN performs better than PSO. The best mean result (shown underlined) is produced by ANN-PSO for every function in this test suite, and this advantage is retained as the dimensionality of the problem increases. This suggests (for these functions at least), that using an ANN to guide PSO is beneficial. For most of the problems, this benefit appears to be quite sizeable.” Benhalem_2020 page 3 col 1 par 4 also states “Our implementation of neural-guided PSO (ANN-PSO) builds upon the standard version of PSO outlined in Algorithm.” As stated, this neural guided PSO builds upon a standard PSO and provides a sizeable benefit to the algorithm. Engelbrecht_2007 discusses using a standard PSO algorithm and ways to optimize such algorithm, such as an inclusion of additional information. Benhalem_2020 does not explicitly teach the inclusion of additional information, but teaches page 3 col 1 par 4: “A key difference is that each particle is assigned an ANN, which, in this paper, is a simple multi-layer Perceptron with one hidden layer.” One reasonably skilled in the art would know that a simple perceptron can be applied to other information in the particle swarm application such as additional information. An individual reasonably skilled in the art would have known that using the Neural guided PSO on top of the standard PSO algorithm of Engelbrecht_2007 with additional information would have produced such benefit with a reasonable expectation of success.
Therefore, it would have been obvious to combine the neural guided PSO of Benhalem_2020 with the standard PSO algorithms of Engelbrcht_2007 for the benefit of better performance to obtain the invention as specified in the claims.
Claim 14
The limitations of claim 14 are substantially the same as those of claim 2 except that it depends on claim 13 and are therefore is rejected due to the same reasons as outlined above for claims 2 and 13.
Claim 15:
The limitations of claim 15 are substantially the same as those of claim 3 except that it depends on claim 13 and are therefore is rejected due to the same reasons as outlined above for claims 3 and 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: “The analysis of financial market risk based on machine learning and particle swarm optimization algorithm” by Tao Liu and Zhongyang Yu discusses the use of particle swarm optimization algorithm in the financial sector.
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/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187